Automatic Extraction of Assembly Component Relationships for Assembly Model Retrieval

AbstractEven if during the Product Design Process, assembly models are described in terms of their constitutive components and associated relationships, only the position of each component is often stored within the Digital Mock-Up. Thus, the mating information are lost. However, these relationships are crucial for many applications, such as retrieval, assembly planning and finite element simulations. In this paper, we propose a method for the detection and use of the mating relationships for assembly model retrieval. The proposed approach detects and analyses the interferences between parts to compute their degree of freedom and kinematic pairs. To support the retrieval of assembly models, the extracted information are formalized and capitalized in a newly proposed hierarchical assembly model descriptor. Results of the application of the method are also provided to show the system capabilities. Moreover, considering that a same joint can be defined in multiple ways, this work provides also a method for retrieving assemblies in a dataset according to the part relationships and their class of equivalence

CAD assembly descriptors for knowledge capitalization and model retrieval

Today, there exists a huge amount of digital data easily downloadable from Internet and/or simply accessible from large databases. Despite this rise, the methods to retrieve and search for specific data have not been sufficiently studied and developed, notably when considering 3D contents. Thus, it is sometime more efficient to define new 3D shapes starting from scratch rather than to try to make use of existing ones hardly identifiable within those databases. This is particularly true when considering CAD assembly models often resulting from a long and time-consuming modeling phase within the Product Development Process. Thus, having new methods, models and tools to capitalize, retrieve and reuse CAD assembly models would help saving a lot of time. This paper addresses such a difficult problem of finding a method to characterize and structure CAD assemblies so as to be able to search for similar ones. A framework has been designed for the retrieval of globally and/or partially similar assembly mod- els according to different user-specified search criteria. It is based on an assembly descriptor, called the Enriched Assembly Model (EAM), which encodes all the required data automatically extracted from the geometry and structure of the CAD models. The data are organized in several layers thus enabling multi-level structuring and queries. It also allows fuzzy queries, which can be further refined

SFINGE 3D: A novel benchmark for online detection and recognition of heterogeneous hand gestures from 3D fingers' trajectories

In recent years gesture recognition has become an increasingly interesting topic for both research and industry. While interaction with a device through a gestural interface is a promising idea in several applications especially in the industrial field, some of the issues related to the task are still considered a challenge. In the scientific literature, a relevant amount of work has been recently presented on the problem of detecting and classifying gestures from 3D hands' joints trajectories that can be captured by cheap devices installed on head-mounted displays and desktop computers. The methods proposed so far can achieve very good results on benchmarks requiring the offline supervised classification of segmented gestures of a particular kind but are not usually tested on the more realistic task of finding gestures execution within a continuous hand tracking session.In this paper, we present a novel benchmark, SFINGE 3D, aimed at evaluating online gesture detection and recognition. The dataset is composed of a dictionary of 13 segmented gestures used as a training set and 72 trajectories each containing 3-5 of the 13 gestures, performed in continuous tracking, padded with random hand movements acting as noise. The presented dataset, captured with a head-mounted Leap Motion device, is particularly suitable to evaluate gesture detection methods in a realistic use-case scenario, as it allows the analysis of online detection performance on heterogeneous gestures, characterized by static hand pose, global hand motions, and finger articulation.We exploited SFINGE 3D to compare two different approaches for the online detection and classification, one based on visual rendering and Convolutional Neural Networks and the other based on geometrybased handcrafted features and dissimilarity-based classifiers. We discuss the results, analyzing strengths and weaknesses of the methods, and deriving useful hints for their improvement. (C) 2020 Elsevier Ltd. All rights reserved

Multi-criteria retrieval of CAD assembly models

Being able to reuse existing design knowledge is of major interest to help designers during the creation of new products. This is true at the level of the parts and even more at the level of the assemblies of multiple parts. Meaningful information and knowledge can be extracted from existing geometric models and associated data and metadata, as well as from the processes followed to define them. This paper proposes a method to characterize and structure CAD assembly models to enable the retrieving of similar models from a database. A framework has been devised for the retrieval of globally and/or partially similar assembly models according to multiple user-specified search criteria. It is based on an assembly descriptor, called the Enriched Assembly Model, which is an attributed graph that encodes all the required data automatically extracted from the geometry and structure of the CAD models. The data are organized in four layers: structural, assembly interface, shape and statistic layers. Starting from a real CAD model or from an abstract query model, the algorithm retrieves models from the database by solving a matching problem. The matching between two assembly models is translated into the problem of finding a sub-isomorphism between two EAMs. The layered organization of the EAM allows partially defined queries, which can be further refined. The effectiveness of the proposed approach is illustrated with results obtained from the developed software prototype

Content-based CAD assembly model retrieval: Survey and future challenges

Currently, the content-based retrieval is a problem of major interest in several different fields and, focusing on mechanical engineering, many approaches exist to compare and retrieve single CAD parts, to evaluate shape similarity, to extract features and to segment models. However, most of the proposed approaches do not take into account all the key characteristics of an assembly model, such as the relationships between its components, and the different levels according to which two assembly models can be considered similar, i.e. either globally, partially, or locally. For these reasons, the retrieval of CAD assembly models still faces challenges to fully satisfy designers’ expectations. The aim of this paper is to review the state-of-the-art of works addressing the CAD assembly model retrieval and to identify future challenges and possible research directions. Firstly, the paper highlights the user requirements for CAD assembly model retrieval and proposes a set of criteria for analyzing the available methods grouped into the following macro-categories: objective, assembly characterization, assembly descriptor, query specification and type of similarity. Secondly, it describes and characterizes the available methods by organizing them according to the adopted criteria. Finally, it discusses the open issues and future challenges

A 3D CAD assembly benchmark

Evaluating the effectiveness of the systems for the retrieval of 3D assembly models is not trivial. CAD assembly models can be considered similar according to different criteria and at different levels (i.e. globally or partially). Indeed, besides the shape criterion, CAD assembly models have further characteristic elements, such as the mutual position of parts, or the type of connecting joint. Thus, when retrieving 3D models, these characteristics can match in the entire model (globally) or just in local subparts (partially). The available 3D model repositories do not include complex CAD assembly models and, generally, they are suitable to evaluate one characteristic at a time and neglecting important properties in the evaluation of assembly similarity. In this paper, we present a benchmark for the evaluation of content-retrieval systems of 3D assembly models. A crucial feature of this benchmark regards its ability to consider the various aspects characterizing the models of mechanical assemblies

Identification of functional components in mechanical assemblies

Manufactured products with different purposes often include similar mechanisms to realize movements required to satisfy specific functionalities. An automatic identification of common mechanisms in assembly models would be a valuable support for analysing or reusing exiting solutions during the design process. In this paper, we present a first step towards the identification of mechanism for motion transformation, focusing on those containing non-linear bearings. In particular, we describe methods for non-linear bearing identification within assemblies, which allow axial rotation, as a shaft rotation. The main novelty concerns the capability of detecting bearings independently on their design level of details, i.e. represented as assemblies of their constituent components or idealized by their external 3D shape outline. The proposed method is based on a set of rules defined according to a priori knowledge and exploits implicit information automatically extracted from the assembly description and can be extended to other types of mechanism

Content-based multi-criteria similarity assessment of CAD assembly models

The use of Digital Mock-Up (DMU) has become mainstream to support the engineering activities all along the Product Development Process. Over the years, companies generate large databases containing digital models and documents related to their products. Considering complex products, the DMU can be composed of several hundred thousand parts assembled together in assembly trees containing tens of sub-assemblies, and representing several terabytes of data. The ability to retrieve existing models is crucial for the competitiveness of companies, as it can help to leverage existing solutions, results and knowledge associated with previous products. To speed up the access to this large amount of reusable information, CAD models search approaches have been proposed, including the so-called content-based search techniques which do not rely on metadata and data organization but exploit the implicit knowledge embedded in the models. As part of a system for the retrieval of CAD assembly models, this paper introduces a set of four measures to evaluate assembly similarities according to multiple criteria. These measures are combined to assess three different levels of similarity (local, partial and global). The local measure only considers the contribution of the parts that are similar in the compared assemblies, while partial and global measures take also into account the number of similar parts compared to the total number of parts in the query and in the target model. Moreover, an ad-hoc visualization interface has been designed to clearly highlight the different similarities and to allow a fast identification of the target models. The validation of the proposed method is discussed, the dataset used to this aim is provided with the specification of the adopted ground truth and some examples of the obtained results are shown

CAD assembly descriptors for knowledge capitalization and model retrieval

Today, there exists a huge amount of digital data easily downloadable from Internet and/or simply accessible from large databases. Despite this rise, the methods to retrieve and search for specific data have not been sufficiently studied and developed, notably when considering 3D contents. Thus, it is sometime more efficient to define new 3D shapes starting from scratch rather than to try to make use of existing ones hardly identifiable within those databases. This is particularly true when considering CAD assembly models often resulting from a long and time-consuming modeling phase within the Product Development Process. Thus, having new methods, models and tools to capitalize, retrieve and reuse CAD assembly models would help saving a lot of time. This paper addresses such a difficult problem of finding a method to characterize and structure CAD assemblies so as to be able to search for similar ones. A framework has been designed for the retrieval of globally and/or partially similar assembly mod- els according to different user-specified search criteria. It is based on an assembly descriptor, called the Enriched Assembly Model (EAM), which encodes all the required data automatically extracted from the geometry and structure of the CAD models. The data are organized in several layers thus enabling multi-level structuring and queries. It also allows fuzzy queries, which can be further refined